1. Field of the Invention
This invention pertains generally to computed tomography imaging, and more particularly to computed tomography imaging incorporating lung, lobe and fissure image segmentation and analysis.
2. Description of Related Art
Computed Tomography (CT) imaging has been used for in vivo assessment of the location, extent, and progression of lung disease in patients. However, the role of diagnostic imaging has generally been limited to visual inspection in clinical practice. In order to enable quantitative analyses in clinical trials and practice, computer-aided methods are important to perform these analyses routinely and reliably in large patient cohorts.
For analysis to be feasible in clinical practice, reliable automation is needed based on the size of the data sets (>400 cross-sectional images for isotropic voxel spacing). Extraction of the lung fields on CT images is relatively straightforward because of the high contrast with surrounding soft-tissue structures. However, subdivision (segmentation) of the lungs into lobes is much more challenging because they are separated by only a thin layer of tissue (fissure). These fissures are very faint due to partial volume averaging.
Current lung segmentation methods are typically based on attenuation thresholding and region-growing to identify the lung fields from the surrounding tissue. Automated lobar segmentation is much more difficult and a small number of research groups are currently working on this problem. Approaches are typically threshold or edge-based and use some information regarding continuity in the longitudinal direction. Most use limited domain knowledge applied in the form of heuristics in the segmentation algorithm, although we are beginning to see more powerful atlas-matching approaches being developed.
Current approaches have not as yet yielded a robust automated lobar segmentation that is reliable enough to be used routinely in clinical practice. These challenges arise because partial volume averaging of the thin fissural plane leads to very faint, incomplete edges that indicate the lobar boundary. Also, in many patients the fissures are anatomically incomplete (i.e., they do not extend all the way across the lung parenchyma). Anatomic and pathological variants in shape and location of fissures also create problems, particularly when disease such as emphysema is present, as this leads to severe deformities. CT technical factors can also change the fissure appearance and image characteristics. The minor fissure of the right lung is particularly difficult to identify because it tends to run parallel to the scan plane and is more variable in shape.
Accordingly, an object of the present invention is an automated or semi-automated system that provides a rapid and reliable method for identifying lung, lobe, and fissure voxels in CT images, so that quantitative lung assessment can be performed in clinical practice. Another object is a system that provides fissure integrity analysis. A further object is a semi-automated segmentation and editing system for lung segmentation and identification of the fissures that are robust across large patient cohorts.
At least some of the above objectives will be met in the following description.